Archive

Abstract:

Machine learning is concerned with algorithmically discovering styles and also relationships in data, as well as utilizing these to execute jobs such as category and also prediction in a variety of domain names. Our company now launch some pertinent jargon as well as deliver a summary of a handful of sorts of machine learning techniques. The mixed influence of new computing resources and also methods along with a boosting barrage of large datasets, is improving many investigation regions as well as may bring about technological innovations that can be used through billions of people. This paper provides the framework that uses feature models and corresponding labels for machine learning algorithms.

Keywords:

Machine Learning,classification,

Refference:

I. B. Srinivas, Shoban Babu Sriramoju, “A Secured Image Transmission Technique Using Transformation Reversal” in “International Journal of Scientific Research in Science and Technology”, Volume-4, Issue-2, February-2018, 1388-1396 [Print ISSN: 2395-6011| Online ISSN: 2395-602X]
II. D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, Shoban Babu Sriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
III. G. C. de S ´ a, G. L. Pappa, as well as A. A. Freitas. Towards a method for instantly picking and also setting up multi-label classification algorithms. In Proc. GECCO Friend, web pages 1125– 1132, 2017.
IV. G. C. de S ´ a, W. J. Pinto, L. O. Oliveira, and G. L. Pappa. RECIPE: A grammar- based framework for instantly developing category pipes. In Proc. of the EuroGP Conference, webpages 246– 261, 2017.
V. J. Demsar. Statistical evaluations of classifiers over various information collections. J. Mach. Learn. Res., 7:1– 30, 2006.
VI. Kian Kumar S V N Madupu, “Challenges and CloudComputing Environments Towards Big Data”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 203-208, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207277
VII. Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275
VIII. Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271
IX. Kiran Kumar S V N Madupu, “opportunities and challenges towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
X. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
XI. Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
XII. Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
XIII. Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
XIV. M. Feurer, A. Klein, K. Eggensperger, et cetera. Dependable as well as sturdy automated ma- chine learning. In Proc. of the International Meeting on Neural Data Processing Systems, webpages 2755– 2763, 2015.
XV. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131. doi: 10.1109/I-SMAC.2018.8653669
XVI. Pushpa Mannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XVII. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XVIII. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017

XIX. PushpavathiMannava, “Research Challenges and Technology Progress of Data Mining with Bigdata”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume5 Issue 4, pp. 08-315, July-August 2019. Available at doi : https://doi.org/10.32628/CSEIT206274
XX. Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XXI. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XXII. Shoban Babu Sriramoju, Naveen Kumar Rangaraju, Dr .A. Govardhan, “An improvement to the Role of the Wireless Sensors in Internet of Things” in “International Journal of Pure and Applied Mathematics”, Volume 118, No. 24, 2018, ISSN: 1314-3395 (on-line version), url: http://www.acadpubl.eu/hub/
XXIII. Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXIV. Srinivas, MonelliAyyavaraiah, Shoban Babu Sriramoju, “A Review on Security Threats and Real Time Applications towards Data Mining” in “International Journal of Pure and Applied Mathematics”, Volume 118, No. 24, 2018, ISSN: 1314-3395 (on-line version), url: http://www.acadpubl.eu/hub/

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Abstract:

In the cloud service situation, the planning, as well as also details, is shifting into the cloud, leading to the lack of trust between clients as well as additionally cloud business. Possessing claimed that the present research on Cloud computing is mainly concentrated on the service side. At the same time, the data securities, as well as trust, have undoubtedly not been adequately looked into yet. This paper checks out into the information security issues from the info life cycle, which includes five steps when a firm makes use of Cloud computing. An info management framework is given out, featuring certainly not merely the data classification having said that also the hazard administration framework.

Keywords:

Cloud computing,big data, privacy,security,

Refference:

I. D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, Shoban Babu Sriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
II. Kiran Kumar S V N Madupu, “Challenges and CloudComputing Environments Towards Big Data”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 203-208, 2014. Available at doi :https://doi.org/10.32628/IJSRSET207277
III. Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275
IV. Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271
V. Kiran Kumar S V N Madupu, “Opportunities and Challenges towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255

VI. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
VII. Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
VIII. Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
IX. Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
X. Lowensohn, J. & McCarthy, C. (2009). Lessons from Twitter’s Security Breach. Available online at: http://news.cnet.com/8301-17939_109-10287558-2.html (Accessed on: November 29, 2012).
XI. Microsoft Security Bulletin MS07-049. Vulnerability in Virtual PC and Virtual Server Could Allow Elevation of Privilege (937986). URL: http://www.microsoft.com/technet/security/bulletin/ms07- 049.mspx. (November 13, 2007) (Accessed on November 20, 2012).
XII. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131. doi: 10.1109/I-SMAC.2018.8653669
XIII. Molnar, D. & Schechter, S. (2010). Self Hosting vs. Cloud Hosting: Accounting for the Security Impact of Hosting in the Cloud. In Proceedings of the Workshop on the Economics of Information Security, 2010, Harvard University, USA, June 2010.
XIV. PushpavathiMannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278

XV. PushpavathiMannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi :https://doi.org/10.32628/IJSRST207254
XVI. PushpavathiMannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XVII. PushpavathiMannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XVIII. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XIX. PushpavathiMannava, “Research Challenges and Technology Progress of Data Mining with Bigdata”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume5 Issue 4, pp. 08-315, July-August 2019. Available at doi : https://doi.org/10.32628/CSEIT206274
XX. Security Tracker. VMWare Shared Folder Bug Lets Local Users on the Guest OS Gain Elevated Privileges on the Host OS. Security Tracker ID: 1019493. URL: http://securitytracker.com/id/1019493 (Accessed on: November 20,2012)
XXI. Xen Vulnerability. URL: http://secunia.com/advisories/26986/. (Accessed on: November 20, 2012).
XXII. Zetter, K. (2010). Google hackers Targeted Source Code of More Than 30 Companies. Wired Threat Level. January 13 2010. Available online at: http://www.wired.com/threatlevel/2010/01/google-hack- attack/ (Accessed on: November 29, 2012).

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Abstract:

Usually, Cloud Computing companies are supplied through a 3rd party service provider who has the infrastructure. Cloud Computing holds the possibility to get rid of the demands for developing of high-cost computing facilities for IT-based alternatives as well as services that the industry utilizes. It assures the delivery of a flexible IT architecture, only available via web coming from light in weight portable devices. This would certainly enable a multi-fold boost in the ability as well as capabilities of the existing and brand new software. This all-new financial concept for computing has uncovered productive ground as well as additionally is luring substantial worldwide assets. A lot of business, like economic, health care and also learning are transferring towards the cloud due to the efficiency of services provided by the pay-per-use style based upon the resources such as refining electricity utilized, bargains conducted, bandwidth absorbed, details moved, or even keeping place taking up, etc. In a cloud computing setup, the entire reports dwell over a collection of online information, allowing the stories to become accessed with digital devices. This file mostly takes note of house components in addition to cultivating computing concepts towards cloud computing.

Keywords:

Cloud computing,architecture,components,

Refference:

I. D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, Shoban Babu Sriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
II. Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275
III. Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi: https://doi.org/10.32628/CSEIT206271
IV. Kiran Kumar S V N Madupu, “Opportunities and Challenges Towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255

V. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
VI. Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
VII. Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
VIII. Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
IX. M. M. Alabbadi, “Cloud Computing for Education and Learning: Education and Learning as a Service (ELaaS),” 2011 14th InternationalConferenceon InteractiveCollaborativeLearning(ICL), pp. 589 – 594, DOI=21-23 Sept.2011.
X. Naresh Kumar, S., Pramod Kumar, P., Sandeep, C.H. & Shwetha, S. 2018, “Opportunities for applying deep learning networks to tumour classification”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 742-747.
XI. Pramod Kumar, P., Sandeep, C.H. & Naresh Kumar, S. 2018, “An overview of the factors affecting handovers and effective highlights of handover techniques for next generation wireless networks”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 722-725.
XII. P. Pramod Kumar, S. Naresh Kumar, Ch. Sandeep, “FOR 4G HETEROGENEOUS NETWORKS A COMPARATIVE STUDY ON VERTICAL HANDOVER DECISION ALGORITHMS”, JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, Vol.-15, No.-6, June(2019) pp 201-212

XIII. P. Pramod Kumar, S. Naresh Kumar, V. Thirupathi, Ch. Sandeep, “QOS AND SECURITY PROBLEMS IN 4G NETWORKS AND QOS MECHANISMS OFFERED BY 4G”, International Journal of Advanced Science and Technology, Vol. 28, No. 20, (2019), pp. 600-606

XIV. P. Pramod Kumar, K Sagar, “FLEXIBLE VERTICAL HANDOVER DECISION ALGORITHM FOR HETEROGENOUS WIRELESS NETWORKS IN 4G”, JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, Vol.-14, No.-6, November -December (2019) pp 54-66

XV. P Pramod Kumar and K Sagar 2019, “A Relative Survey on Handover Techniques in Mobility Management”, IOP Conf. Ser.: Mater. Sci. Eng.594 012027
XVI. PushpavathiMannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XVII. Pushpa Mannava, “Research Challenges and Technology Progress of Data Mining with Bigdata”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume5 Issue 4, pp. 08-315, July-August 2019. Available at doi : https://doi.org/10.32628/CSEIT20627
XVIII. Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XIX. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XX. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XXI. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
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XXIII. Ramesh Gadde, Namavaram Vijay, “A SURVEY ON EVOLUTION OF BIG DATA WITH HADOOP” in “International Journal of Research In Science & Engineering”, Volume: 3 Issue: 6 Nov-Dec 2017.
XXIV. Sandeep, C.H., Naresh Kumar, S. & Pramod Kumar, P. 2018, “Security challenges and issues of the IoT system”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 748-753.
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XXVII. Shailaja, P., Guru Rao, C.V. &Nagaraju, A. 2019, “A parametric oriented research on routing algorithms in mobile adhoc networks”, International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 1, pp. 4116-4126.
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XXX. Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXXI. Sriramoju Ajay Babu, Namavaram Vijay and Ramesh Gadde, “An Overview of Big Data Challenges, Tools and Techniques”in “International Journal of Research and Applications”, Oct – Dec, 2017 Transactions 4(16): 596-601
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XXXIV. Venkatramulu, S. & Rao, Chakunta. (2018). CSES: Cuckoo Search Based Exploratory Scale to Defend Input-Type Validation Vulnerabilities of HTTP Requests. 10.1007/978-981-10-8228-3_23.Venkatramulu, S. & Guru Rao, C.V. 2017, “RPAD: Rule based pattern discovery for input type validation vulnerabilities detection & prevention of HTTP requests”, International Journal of Applied Engineering Research, vol. 12, no. 24, pp. 14033-14039.
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XXXVI. W. Itani, A. Kayssi, and A. Chehab, “Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures,” 2009 8th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2009, pp.711-716.

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Abstract:

Currently, our business performs not know just how to configure pc systems if you want to find out a lot more dependable personally. Although the techniques that have been learned operate very successfully for certain features, certainly not suited for all purposes. As an example, machine learning algorithms are, in fact, generally utilized in information mining. Likewise, in sites where documents are involved, these algorithms work and also lead far better than some other methods. As an example, in concerns featuring pep talk awareness, algorithms based on machine learning resulted better than the various different strategies. Delivered the unpredicted routine of data as well as calculating details, there prevails restored interest in administering data-driven machine learning strategies to problems for which the advancement of traditional style responses is, in fact-checked by means of modeling or even algorithmic deficiencies. This paper briefly goes over regarding the category of ML algorithms as well as additionally intersection of stats and computer science in machine learning.

Keywords:

Machine learning,algorithms,intersection,

Refference:

I. A. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131.doi: 10.1109/I-SMAC.2018.8653669
II. D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technology Letters, vol. 28, no. 19, pp. 2102–2105, Apr. 2016.
III. D. Wang, M. Zhang, Z. Li, Y. Cui, J. Liu, Y. Yang, and H. Wang, “Nonlinear decision boundary created by a machine learning-based classifier to mitigate nonlinear phase noise,” in European Conference on Optical Communication (ECOC) 2015, Oct. 2015, pp. 1–3.
IV. D. Deepika, a Krishna Kumar, Monelli Ayyavaraiah, Shoban Babu Sriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
V. F. Lu, P.-C. Peng, S. Liu, M. Xu, S. Shen, and G.-K. Chang, “Inte- gration of Multivariate Gaussian Mixture Model for Enhanced PAM-4 Decoding Employing BasisExpansion,”in OpticalFiberCommunications Conference (OFC) 2018, Mar.2018.
VI. Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275
VII. Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271

VIII. Kiran Kumar S V N Madupu, “Opportunities and Challenges Towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
IX. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
X. Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
XI. Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
XII. Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
XIII. M. E. McCarthy, N. J. Doran, and A. D. Ellis, “Reduction of Non- linear Intersubcarrier Intermixing in Coherent Optical OFDM by a Fast Newton-Based Support Vector Machine Nonlinear Equalizer,” IEEE/OSAJournalofLightwaveTechnology,vol.35,no.12,pp.2391– 2397, Mar.2017.
XIV. Naresh Kumar, S., Pramod Kumar, P., Sandeep, C.H. & Shwetha, S. 2018, “Opportunities for applying deep learning networks to tumour classification”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 742-747.
XV. Pramod Kumar, P., Sandeep, C.H. & Naresh Kumar, S. 2018, “An overview of the factors affecting handovers and effective highlights of handover techniques for next generation wireless networks”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 722-725.
XVI. Pushpavathi Mannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XVII. Pushpa Mannava, “Research Challenges and Technology Progress of Data Mining with Bigdata”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume5 Issue 4, pp. 08-315, July-August 2019. Available at doi : https://doi.org/10.32628/CSEIT20627
XVIII. Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XIX. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XX. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XXI. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XXII. Ramesh Gadde, Namavaram Vijay, “A SURVEY ON EVOLUTION OF BIG DATA WITH HADOOP” in “International Journal of Research In Science & Engineering”, Volume: 3 Issue: 6 Nov-Dec 2017.
XXIII. Sandeep, C.H., Naresh Kumar, S. & Pramod Kumar, P. 2018, “Security challenges and issues of the IoT system”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 748-753.
XXIV. Seena Naik, K. & Sudarshan, E. 2019, “Smart healthcare monitoring system using raspberry Pi on IoT platform”, ARPN Journal of Engineering and Applied Sciences, vol. 14, no. 4, pp. 872-876.
XXV. Sheshikala, M., Kothandaraman, D., Vijaya Prakash, R. & Roopa, G. 2019, “Natural language processing and machine learning classifier used for detecting the author of the sentence”, International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 936-939.
XXVI. Shailaja, P., Guru Rao, C.V. & Nagaraju, A. 2019, “A parametric oriented research on routing algorithms in mobile adhoc networks”, International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 1, pp. 4116-4126.
XXVII. Sivakumar, M., Ramakrishna, M.S., Subrahmanyam, K.B.V. & Prabhandini, V. 2017, “Model Order Reduction of Higher Order Continuous Time Systems Using Intelligent Search Evolution Algorithm”, Proceedings – 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies, ICRTEECT 2017, pp. 70.
XXVIII. Shailaja, G.K. & Rao, C.V.G. 2019, “Robust and lossless data privacy preservation: optimal key based data sanitization”, Evolutionary Intelligence.
XXIX. Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXX. Sriramoju Ajay Babu, Namavaram Vijay and Ramesh Gadde, “An Overview of Big Data Challenges, Tools and Techniques”in “International Journal of Research and Applications”, Oct – Dec, 2017 Transactions 4(16): 596-601
XXXI. Srinivas, Chintakindi & Rao, Chakunta & Radhakrishna, Vangipuram. (2018). Feature Vector Based Component Clustering for Software Reuse. 1-6. 10.1145/3234698.3234737.
XXXII. Subba Rao, A. & Ganguly, P. 2018, “Implementation of Efficient Cache Architecture for Performance Improvement in Communication based Systems”, International Conference on Current Trends in Computer, Electrical, Electronics and Communication, CTCEEC 2017, pp. 1192.
XXXIII. Venkatramulu, S. & Rao, Chakunta. (2018). CSES: Cuckoo Search Based Exploratory Scale to Defend Input-Type Validation Vulnerabilities of HTTP Requests. 10.1007/978-981-10-8228-3_23.Venkatramulu, S. & Guru Rao, C.V. 2017, “RPAD: Rule based pattern discovery for input type validation vulnerabilities detection & prevention of HTTP requests”, International Journal of Applied Engineering Research, vol. 12, no. 24, pp. 14033-14039.
XXXIV. X. Lu, M. Zhao, L. Qiao, and N. Chi, “Non-linear Compensation of Multi-CAP VLC System Employing Pre-Distortion Base on Clustering of Machine Learning,” inOptical Fiber CommunicationsConference (OFC) 2018, Mar.2018.
XXXV. Z. Ghassemlooy, and N. J. Doran, “Artificial neural network nonlinear equalizer for coherent optical OFDM,” IEEE Photonics Technology Letters, vol. 27, no. 4, pp. 387–390, Feb. 2015.

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Abstract:

A ubiquitous process to evoke the most needed data information from huge amount of unprocessed data to analyze the patterns is called as data mining which is also named as data through knowledge discovery. It helps the enterprises to extract the data information to gain knowledge for better. [I] Data mining usually deals with text for mining. Since we are using internet for the accessibility of data. In other words, we are making use of web to extract the data, modify and process the text using the WebPages. Evoking the information data which is present on internet is done using data mining is called as Web Mining.[II] It is an integral part of data mining for searching and analyzing the pattern. There are various data resources to obtain the data from web which is categorized into metadata, text documents, web links   and web content. A web mining also consist of images, videos and audio information data which are considered as multimedia data. As, many users are more keen towards extracting information in form of images and videos from the web pages , so  there’s a need of  bringing out the required multimedia  data information from unused scattered multimedia data present in the web. Here, we need to coalesce mining concepts through web into the multimedia stored data. Such concept is considered as Multimedia Web Mining, [V]It reaps the hidden information of a multimedia file as metadata, represents relationship between multimedia data files^]5. For better and efficient working performance of mining techniques, multimedia mining also index and classify the various modes of multidata such as animation, moving, still , playback  and video modes. Multimedia information is divided into two halves as organized and semi organized. Similarly web mining is categorized into utilization mining, organized mining and substance mining. In this paper, we explore the integration of multimedia with web mining for better enhancement in achieving the classification of data.

Keywords:

Web mining,levels of data mining,

Refference:

I. http://airccse.org/journal/ijcga/papers/5115ijcga05.pdf
II. https://www.researchgate.net/publication/319404075_A_Survey_on_Web_Mining_Techniques_and_Applications
III. https://www.researchgate.net/publication/230639907_A_Survey_on_Multimedia_Data_Mining_and_Its_Relevance_Today
IV. http://www.ijcstjournal.org/volume-5/issue-3/IJCST-V5I3P21.pdf
V. http://www.academia.edu/Documents/in/Web_Data_Mining
VI. https://ieeexplore.ieee.org/abstract/document/5992597
VII. https://arxiv.org/pdf/1109.1145
VIII. Kiran Kumar S V N Madupu, “Opportunities and Challenges Towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
IX. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
X. Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
XI. Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
XII. Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
XIII. M. M. Alabbadi, “Cloud Computing for Education and Learning: Education and Learning as a Service (ELaaS),” 2011 14th InternationalConferenceonInteractiveCollaborativeLearning(ICL), pp. 589 – 594, DOI=21-23 Sept.2011.
XIV. Naresh Kumar, S., Pramod Kumar, P., Sandeep, C.H. & Shwetha, S. 2018, “Opportunities for applying deep learning networks to tumour classification”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 742-747.
XV. Pramod Kumar, P., Sandeep, C.H. & Naresh Kumar, S. 2018, “An overview of the factors affecting handovers and effective highlights of handover techniques for next generation wireless networks”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 722-725.
XVI. Pramod Kumar P,Thirupathi V, Monica D, “Enhancements in Mobility Management for Future Wireless Networks”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2,Issue 2, February 2013

XVII. Pramod Kumar P, CH Sandeep, Naresh Kumar S, “An Overview of the Factors Affecting Handovers and EffectiveHighlights of Handover Techniques for Next GenerationWireless Networks”, Indian Journal of Public Health Research &Development, November 2018, Vol.9, No. 11

XVIII. P. Pramod Kumar, K. Sagar, “Vertical Handover Decision Algorithm Based On Several Specifications in Heterogeneous Wireless Networks”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-9, July 2019

XIX. P. Pramod Kumar ,Dr. K. Sagar, “A proficient and smart electricity billing management system ” ,International Conference on Emerging Trends in Engineering and published in Springer Nature as a Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3), July 2019.
XX. PushpavathiMannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XXI. Pushpa Mannava, “Research Challenges and Technology Progress of Data Mining with Bigdata”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume5 Issue 4, pp. 08-315, July-August 2019. Available at doi : https://doi.org/10.32628/CSEIT20627
XXII. Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XXIII. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XXIV. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XXV. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XXVI. P. Kalagiakos “Cloud Computing Learning,” 2011 5th International Conference on Application of Information and Communication Technologies (AICT), Baku pp. 1 – 4, DOI=12-14 Oct.2011.
XXVII. Ramesh Gadde, Namavaram Vijay, “A SURVEY ON EVOLUTION OF BIG DATA WITH HADOOP” in “International Journal of Research In Science & Engineering”, Volume: 3 Issue: 6 Nov-Dec 2017.
XXVIII. Sandeep, C.H., Naresh Kumar, S. & Pramod Kumar, P. 2018, “Security challenges and issues of the IoT system”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 748-753.
XXIX. Seena Naik, K. & Sudarshan, E. 2019, “Smart healthcare monitoring system using raspberry Pi on IoT platform”, ARPN Journal of Engineering and Applied Sciences, vol. 14, no. 4, pp. 872-876.
XXX. Sheshikala, M., Kothandaraman, D., Vijaya Prakash, R. & Roopa, G. 2019, “Natural language processing and machine learning classifier used for detecting the author of the sentence”, International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 936-939.
XXXI. Shailaja, P., Guru Rao, C.V. &Nagaraju, A. 2019, “A parametric oriented research on routing algorithms in mobile adhoc networks”, International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 1, pp. 4116-4126.
XXXII. Sivakumar, M., Ramakrishna, M.S., Subrahmanyam, K.B.V. &Prabhandini, V. 2017, “Model Order Reduction of Higher Order Continuous Time Systems Using Intelligent Search Evolution Algorithm”, Proceedings – 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies, ICRTEECT 2017, pp. 70.
XXXIII. Shailaja, G.K. & Rao, C.V.G. 2019, “Robust and lossless data privacy preservation: optimal key based data sanitization”, Evolutionary Intelligence.
XXXIV. Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXXV. Sriramoju Ajay Babu, Namavaram Vijay and Ramesh Gadde, “An Overview of Big Data Challenges, Tools and Techniques”in “International Journal of Research and Applications”, Oct – Dec, 2017 Transactions 4(16): 596-601
XXXVI. Srinivas, Chintakindi& Rao, Chakunta& Radhakrishna, Vangipuram. (2018). Feature Vector Based Component Clustering for Software Reuse. 1-6. 10.1145/3234698.3234737.
XXXVII. Subba Rao, A. &Ganguly, P. 2018, “Implementation of Efficient Cache Architecture for Performance Improvement in Communication based Systems”, International Conference on Current Trends in Computer, Electrical, Electronics and Communication, CTCEEC 2017, pp. 1192.
XXXVIII. Venkatramulu, S. & Rao, Chakunta. (2018). CSES: Cuckoo Search Based Exploratory Scale to Defend Input-Type Validation Vulnerabilities of HTTP Requests. 10.1007/978-981-10-8228-3_23.Venkatramulu, S. & Guru Rao, C.V. 2017, “RPAD: Rule based pattern discovery for input type validation vulnerabilities detection & prevention of HTTP requests”, International Journal of Applied Engineering Research, vol. 12, no. 24, pp. 14033-14039
XXXIX. W. Dawoud, I. Takouna, and C. Meinel, “Infrastructure as a Service Security: Challenges and Solutions,” 2010 7thInternational Conference on Informatics and System, pp. 1-8, March2010.
XL. W. Itani, A. Kayssi, and A. Chehab, “Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures,” 2009 8th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2009, pp.711-716.

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Abstract:

In today’s generation, the internet has become an essential part of our lives for communication, banking and studying. Especially the youth has turned them into the virtual world. Have you ever speculated how many people stalk you on social media? With the rapid usage of the internet by society, it is also important to protect the information. A computer should have security in it if not it will be accessed by hackers. A hacker can illegally access the data present in a computer. Hacking the important data affects our lives adversely. Cyber-attacks are generally planned wisely. The cyber security specialists and cybercriminals started the competition which will be compared with the growth of offensive weapons and defensive ones to resist the attacks. Cyber security is that the field of science that's developing perpetually and speedily. A Cybercrime square measures currently a worldwide downside that affects innumerable spheres of human life. Every new appliance and software package become the target for cyber criminals sooner or later, therefore their makers do everything doable to be one step ahead. Nearly everything we have a tendency to see in our everyday life may require a number of the cyber security. The main ambition of the hackers is to steal confidential information or to change the data. The hackers opt for a unique way to infect the computer to gain access to it. They usually use malicious software to infect the computer. A virus is been carried by the attachments of the e-mails. When we download these attachments, the computer gets infected. Cyber security plays a major role in organizations such as governments, businesses, hospitals as these have a wide range of confidential information with them. Social networking sites became the medium for sharing information and connecting with people. One side we have an advantage as it connects people, on the other hand, it creates opportunities for cybercrimes. As an individual, we should be alert enough to secure our accounts and data.

Keywords:

cyber security ,cybercrimes,cyberattacks,ransom ware,malicious software,hackers,

Refference:

I. Azzah Kabbas, Atheer Alharthi, and Asmaa Munshi, Artificial Intelligence Applications in Cybersecurity, IJCSNS International Journal of Computer Science and Network Security, 20(2),pp.120-124, Fabruary 2020.
II. Clifton L. Smith, David J. Brooks, Security Risk Management in Security Science, 2013.
III. G.Sunil, Srinivas Aluvala,K. Ravi Chythanya, Goje. Roopa, Rajesh Mothe, Trends having huge impact on cyber security and techniques of cyber security, International Journal of Advanced Science and Technology, 29(2), pp.2701-2708, Jan.2020.
IV. G. Sunil, Srinivas Aluvala, S. Tharun Reddy, Dadi Ramesh, Dr. Revuri Varun, Various forms of cybercrime and role of social media in cyber security, International Journal of Advanced Science and Technology, 29(2), pp.2709-2715, Jan.2020.
V. G.Sunil, Srinivas Aluvala, Nagendhar Yamsani, Kanegonda Ravi Chythanya, Srikanth Yalabaka, Security Enhancement of Genome Sequence Data in Health Care Cloud, International Journal of Advanced Trends in Computer Science and Engineering, 8(2), pp.328-332, March-April 2019.
VI. Kenichi Yoshida, Kazuhiko Tsuda,Setsuya Kurahashi,Hiroki Azuma, Online Shopping Frauds Detecting System and Its Evaluation, 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), 4-8 July 2017.
VII. Lisa Lee Bryan, Effective Information Security Strategies for Small Business, International Journal of Cyber Criminology, 14(1), pp.341-360, January-June (2020).
VIII. Mohamed Abomhara, Geir M. Koien, Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks, Journal of Cyber Security, 4, pp. 65–88, 22 May 2015.
IX. Roopa Goje, Ramesh Dadi, Designing a collaborative detection system for detecting the threats to the cyber security in big data, Indian Journal of Public Health research & Development, 9(11), pp.730-733, November 2018.
X. Surbhi Guptha, Abhishek Singhal, Akanksha Kapoor, A literature survey on social engineering attacks: Phishing attack, 2016 International Conference on Computing, Communication and Automation (ICCCA), 29-30 April 2016.
XI. Yashpal Singh Bist, Charu Agarwal, Uttara Bansal, Online Business Frauds: A Case Study of an Online Fraud Survey Company, International Journal of Modern Engineering Research (IJMER), 2(6), Nov-Dec. 2012 pp-4396-4404.

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Abstract:

Now days, Security plays an important role in day-to-day life.  The use of the internet in human life has become the day to day activity and with the internet the use of automation devices has increased. All transaction needs to secure authentication to complete. Hence, we have introduced a Face Recognition method. It can apply in many fields such as to authenticate users, security issues etc., It mainly plays a significant role in real time surveillance systems. We implemented the Convolution neuron network to automatically create dataset and recognition with the graphical user interface. Before creating a dataset the system takes permission from the user then it creates the dataset and trains the model for farther authentication.

Keywords:

Security,deep learning,neural network,authentication,

Refference:

I E. I. Abbas, M. E. Safi And K. S. Rijab, “Face Recognition Rate Using Different Classifier Methods Based On Pca,” 2017 International Conference On Current Research In Computer Science And Information Technology (Iccit), Slemani, 2017, Pp. 37-40, Doi: 10.1109/Crcsit.2017.7965559.

II H.-W. Ng, S. Winkler. A Data-Driven Approach to Cleaning Large Face Datasets. Proc. Ieee International Conference on Image Processing (Icip), Paris, France, Oct. 27-30, 2014.

III M. R. Reshma and B. Kannan, “Approaches On Partial Face Recognition: A Literature Review,” 2019 3rd International Conference on Trends In Electronics And Informatics (Icoei), Tirunelveli, India, 2019, Pp. 538-544, Doi: 10.1109/Icoei.2019.8862783.

IV O. M. Parkhi, A.Vedaldi, A. Zisserman Deep Face Recognition British Machine Vision Conference, 2015.

V Praveen P., Rama B(2020). “An Optimized Clustering Method To Create Clusters Efficiently” Journal Of Mechanics Of Continua And Mathematical Sciences, ISSN (Online): 2454 -7190 Vol.-15, No.-1, January (2020) pp 339-348 ISSN (Print) 0973-8975,https://doi.org/10.26782/jmcms.2020.01.00027 .

VI P.Kumara Swamy, Dr.C.V.Guru Rao, Dr.V.Janaki, “Functioning Of Secure Key Authentication Scheme In” In International Journal Of Pure And Applied Mathemat, Volume 118, Issue 14, Page No(S) 27 – 32, MAR. 2018, [ISSN(Print):1314-3395].

VII R. Prema and P. Shanmugapriya, “A Review: Face Recognition Techniques For Differentiate Similar Faces and Twin Faces,” 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (Icecds), Chennai, 2017, Pp. 2899-2902, Doi: 10.1109/Icecds.2017.8389985.

VIII Sharmila, Raman Sharma, Dhanajay Kumar, Vaishali Puranik, Kritika Gautham,”Performance Analysis Of Human Face Recognition Techniques” In 2019 Ieee

IX Sharma, Sudha and Soni, Alpesh and Malviya, Vijay, Face Recognition Based On Convolution Neural Network (Cnn) Applications in Image Processing: A Survey (April 15, 2019). Proceedings of Recent Advances in Interdisciplinary Trends in Engineering & Applications (Raitea) 2019.

X Surface[3D] Measurement Through Easy-Snap Phase Shift Fringe Projection.” Springerprofessional.De,Https://Www.Springerprofessional.De/En/3d-Surface-Measurement-Through-Easy-Snap-Phase-Shift-Fringe-Proj/15447362. Accessed 26 Mar. 2020.

XI Sallauddin Md Et. “A Comprehensive Study on Traditional Ai and Ann Architecture.” International Journal of Advanced Science and Technology, Vol. 28, No. 17, Dec. 2019, Pp. 479–87.

XII Yerrolla Chanti, Kothanda Raman, K. Seenanaik, Dandugudum Mahesh, B.Bhaskar” An Enhanced On Bidirectional LI-FI Attocell Access Point Slicing and Virtualization Using Das2 Conspire” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S3, July 2019.

XIII Yerrolla Chanti, Dr. K. Seena Naik2, Rajesh Mothe3, Nagendar Yamsani4, Swathi Balija5” A Modified Elliptic Curve Cryptography Technique For Securing Wireless Sensor Networks” International Journal Of Engineering &Technology 2018.

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Abstract:

IoT devices, or any of the various problems inside the net update, are nonstandardregisteringdevicesthatbepartofremotelyuptodate a network and highlight theopportunityupdatetransmitstatistics[III].IoTconsistsof growing internet community past gadgets,whichcontainpcsupdated,workstations,cellphonesandmedicines, uptodateanyassortmentofactuallymoronicornonnetempowered bodily gadgets andpopularupdate.Implantedwithage, those devices can talk andfunction connection over the net, and they might be remotely located and overseen [X].To updatedis coupononline communication, being Growingage,hasupdate an appealing area of research in audiosystemhoweverPromising packages like quick assortmentcontactless discussion for mobilephone and different superior devices the same. Rigt now, valid facts and direction of NFC is up-to-date be beautifully save updated up for the headway of capacity and up to date reduce the scaffold hollow between its critical Online and alertness exercise. Proper now, proposed up-to-date NFC might be applied for sharing little evaluations along with contacts, and bootstrapping rapid institutions with percentage larger media and various records and boat Wi-Fi wireless, software content material fabric, contactless installments, examine NFC labels amongst advanced gadgets [II][I]. We more over have investing the NFC corporation business enterprise natural system and present day destiny market propensities. In diverse terms this compressive in NFC wireless duration manages advancement of statistics.

Keywords:

NFC,IOT,RIFD,BLUETOOTH.,

Refference:

I APC, Inside NFC: how near field communication works. August 17, 2011. http://apcmag.com/insidenfc-how-near-field-communication-works.htm.

II Bura Vijay Kumar1, Yerrolla Chanti2, D. Kothandaraman3, A. Harshavardhan4, Sangameshwar Kanugula5 S” INTERNET OF THINGS MIDDLEWARE ARCHITECTURE FOR COMMUNICATION” Studia Rosenthaliana (Journal for the Study of Research) ISSN NO: 0039-3347.dec 2019.

III D. Kothandaraman1, Y. Chanti2, B. Vijaykumar3, A. Harshavardhan4, K. Seena Naik5” Indoor Users Motion Direction Detection Using Orientation Sensor with BLE in Internet of Things” Studia Rosenthaliana (Journal for the Study of Research) ISSN NO: 0039-3347.dec 2019.

IV D.M. Monteiro, J.J.P.C. Rodrigues, J. Lloret, “A Secure NFC Application for Credit Transfer among Mobile Phones”, International Conference on Computer, Information and Telecommunication Systems (CITS), 2012, pp. 1- 5.

V E. Desai, M.G. Shajan, “A Review on the Operating Modes of Near Field Communication”, International Journal of Engineering and Advanced Technology (IJEAT), Volume-2, Issue-2, 2012. Ber Security (CIACS), 2014, pp. 35- 38.

VI E. Macias, J. Wyatt, “NFC Active and Passive Peer-toPeer Communication Using the TRF7970A”, April 2014, http://www.ti.com/lit/an/sloa192/sloa192.pdf.

VII Internet source ofWikipedia .com

VIII K. Seena Naik and E. Sudarshan ”Smart Healthcare Monitoring System using Raspberry Pi on IoT Platform” ARPN Journal of Engineering and Applied Sciences ©2006-2019 Asian Research Publishing Network (ARPN). All rights reserved. VOL. 14, NO. 4, FEBRUARY 2019. ISSN 1819-66.

IX [N.A. Chattha. “NFC – Vulnerabilities and Defense” Conference on Information Assurance and Cyber Security (CIACS), 2014, pp. 35- 38.

X P. V. Nikitin. “An Overview of Near Field UHF RFID,” in Proc. IEEE Int. Conf. RFID, Mar. 2007, pp. 167-174.

XI Shirsha Ghosh, Joyeeta Goswami, Abhishek Kumar and Alak Majumder” Department of Electronics & Communication Engineering, National Institute of Technology, Arunachal Pradesh, Yupia, India” Issues in NFC as a Form of Contactless Communication: A Comprehensive Survey” 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, T.N., India. 6 – 8 May 2015. pp.245-252.

XII V. Coskun, K. Ok, B. Ozdenizci “Near Field Communication, from theory to practice”, Wiley Publication.

XIII Yerrolla Chanti1, Seena Naik Korra2, Bura Vijay Kumar3, A. Harshavardhan4, D. Kothandaraman5 “New Technique using an IoT Robot to Oversight the Smart Domestic Surroundings” Studia Rosenthaliana (Journal for the Study of Research) ISSN NO: 0039-3347.dec 2019.

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Abstract:

The objective of this paper is to solve pure integer programming problems without using Gomorian constraints. In this, CMI method is used for solving linear programming problems instead of simplex method. In CMI method, there is no need to calculate net evaluations, which is essential and mandatory in pre-existing methods. By discarding the calculation of net evaluations, the iterations in the procedure gets reduced or remains atmost equal in number. After getting a non-integer value in final CMI table, here we use a reduction technique instead of adding Gomorian constraint to get the integer solution directly.The main advantage of using this reduction technique is to avoid using, any additional constraints and the Dual simplex method for getting an integer solution. With the elimination of the above processes, the integer solutions are arrived very easily. Hence this new approachof pure integer programming problemensures time conservation at various levels in deriving the optimal solutions.  This proposed method is illustrated withexamples.

Keywords:

CMI Method,LPP,IPP,Optimal Solution,Reduction technique,

Refference:

I G.B. Dantzig, Maximization of linear function of variables subject to linear inequalities Koop man cowls commission Monograph, 1951).

II Handy A.Taha: ‘Operations Research An Introduction’ 8th edition by Pearson Publication.

III Kalpana Lokhande; Pranay.Khobragade and .W. Khobragade: Alternative approach to simplex method, International journal of engineering and innovative Technology, volume 4, Issue 6, pg: 123-127.

IV P.Pandian and M.Jayalakshmi: A new approach for solving a class of pure integer linear programming problems, International journal of advanced engineering technology.

V S.Cynthiya Margaret Indrani and Dr.N.Srinivasan: ‘CMI –M Technique for the solution of linear Programming problem,’ International Journal of Research and Analytical Reviews, October 2018, Volume-5, Issue-4.Pg-76-82ded.

VI S.Cynthiya Margaret Indrani and Dr.N.Srinivasan: ‘CMI Method for the solution of linear formulating problem’, Journal of Emerging Technologies and Innovative Research, September 2018, Volume 5, Issue 9, Pg. 248-253.

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Abstract:

There are high necessities to create counterfeit arms for some brutal circumstances where human communications are displaying difficulties or unrealistic (for example outlandish circumstances). This paper presents data, strategies and methods which are fundamental for building a mechanical arm constrained by the developments of ordinary human arm (Gesture Robotic Arm) whose information is gaining by utilizing the Accelerometer. The improvement of this arm depends on the ARM stage in which all are interfaced with one another by utilizing lpc2148 smaller scale controller. The model of automated arm of this paper has been actualized practically.Thedeveloped mechanical arm of this paper is followed the development of human arm with a decent exactness. Usage of this arm could be normal for beating the issues, for example, picking or setting object that are away from the users.

Keywords:

Gesture Robotic Arm,Motion Perception, Accelerometer,lpc2148 smaller scale controller,

Refference:

I. Aggarwal, L., Gaur, V., & Verma, P., (2013) “Design and Implementation of a Wireless Gesture Controlled Robotic Arm with Vision”, International Journal of ComputerApplications (0975 – 8887), 79 (13), pp. 39–43.
II. Brahmani, K., Roy, K. S., & Ali, M., (2013) “Arm 7 Based Robotic Arm Control by Electronic Gesture Recognition Unit Using MEMS”, International Journal ofEngineering Trends and Technology, 4 (4), pp. 1245–1248.
III. Dadi, R., Sallauddin, Pasha, S.N., Harshavardhan, A. &Kumarawamy, P. 2019, “Adapting best path for mobile robot by predicting obstacle size”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9 Special Issue 2, pp. 200-202.
IV. Deshpande, Vivek, and P. M. George. “Kinematic Modelling and Analysis of 5 DOF Robotic Arm.” International Journal of Robotics Research and Development (IJRRD) 4.2 (2014): 17-24.
V. Dharaskar, R. V., Chhabria, S. A., &Ganorkar, S., (2009) “Robotic Arm Control Using Gesture and Voice”, International Journal of Computer, InformationTechnology&Bioinformatics (IJCITB), 1 (1), pp. 41–46.
VI. Gandhi, K. R. U. T. A. R. T. H., et al. “Motion controlled robotic arm.” International Journal of Electronics and Communication Engineering (IJECE) 2.5 (2013): 81-86.
VII. Humbe, A. B., et al. “Review of laser plastic welding process.” Int. J. Res. Eng. Technol 2 (2014): 191-206.
VIII. Humbe, A. B., P. A. Deshmukh, and M. S. Kadam. “The Review Of Articulated R12 Robot And Its Industrial Applications.” International Journal of Research in Engineering & Technology 2.2 (2014): 113-118.
IX. J.Tarunkumar., P. Ramchander Rao. & M. Sampath Reddy. 2019, “IOT based Email Enabled Smart Home Automation System”, International Journal of Recent Technology and Engineering, vol.8, no.1C2. 80-82.
X. Khajone, S. A., Mohod, S. W., &Harne, V. M., (2015) “Implementation of Wireless Gesture Controlled Robotic Arm”, International Journal of Innovative research in Computer and Communication Engineering, 3 (1), pp. 375–379.
XI. Mohapatro, Gourishankar, Ruby Mishra, and Shah Shubham Kamlesh. “Preliminary Testing And Analysis Of An Optimized Robotic Arm, For Ct Image Guided Medical Procedures.” International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) 7.6, (2017) 239-246
XII. Neto, P., Pires, N. J., & Moreira, P. A., (2009) “Accelerometer-Based Control of anIndustrial Robotic Arm”, International Journal of Electronics, 6, pp. 167 – 173.
XIII. Ramesh, D., Pasha, S.N. &Sallauddin, M. 2019, “Cognitive-based adaptive path planning for mobile robot in dynamic environment”. First International Conference on Artificial Intelligence and Cognitive Computing. Advances in Intelligent Systems and Computing, vol 815. Springer, Singapore.
XIV. Shilpa, N., Sridevi, C. & Anand, M. 2019, “Object tracking robot by using raspberry pi with open computer vision (CV)”, Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 7, pp. 762-766.
XV. Waldherr, S., Romero, R., &Thrun, S., (2000) “A Gesture Based Interface for Human-Robot Interaction”, Autonomous Robots in Springer, 9 (2), pp. 151 – 173.
XVI. Zabbar, Md Ajijul Bin, and ChistyNafiz Ahmed. “Design & Implementation of an Unmanned Ground Vehicle (UGV) Surveillance Robot.” International Journal of Electrical and Electronics Engineering (IJEEE) 5.6 (2016): 2278-9944.

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Abstract:

This paper suggests a scheme for trajectory tracking on a two wheeled mobile robot using integral sliding mode control method in the presence of external disturbances and inertia uncertainties. In this study the modified adaptive sliding mode controller for nonholonomic wheeled mobile robot is developed. Nonlinear control used to combine the kinematic and dynamic controller to follow the desired path. Firstly, the desired path is created. Secondly, the kinematic tracking controller used linear and angular velocities form reference model and feeds posture and velocities errors as input term in the sliding controller. Finally, the dynamic control was used to follow the path. Proposed control system is verified and validated using MATLAB\SIMULINK to track the required WMR trajectory and it is shown that the suggested system has better transient efficiency on different trajectories with acceptable steady stateerror.

Keywords:

Wheeled mobile robot,dynamic uncertainty,Kinematic and dynamic controller,Dynamic control,Transient efficiency,

Refference:

I Al-Araji, Ahmed S., & Ibraheem, B. A. (2019). A Comparative Study of Various Intelligent Optimization Algorithms Based on Path Planning and Neural Controller for Mobile Robot. Journal of Engineering, 25(8), 80–99. https://doi.org/10.31026/j.eng.2019.08.06

II Al-Araji, Ahmed Sabah. (2014). Development of kinematic path-tracking controller design for real mobile robot via back-stepping slice genetic robust algorithm technique. Arabian Journal for Science and Engineering, 39(12), 8825–8835.

III Antonelli, G., Chiaverini, S., & Fusco, G. (2007). A fuzzy-logic-based approach for mobile robot path tracking. IEEE Transactions on Fuzzy Systems, 15(2), 211–221.

IV Bessas, A., Benalia, A., & Boudjema, F. (2016). Integral sliding mode control for trajectory tracking of wheeled mobile robot in presence of uncertainties. Journal of Control Science and Engineering, 2016.

V Binh, N. T., Tung, N. A., Nam, D. P., & Quang, N. H. (2019). An adaptive backstepping trajectory tracking control of a tractor trailer wheeled mobile robot. International Journal of Control, Automation and Systems, 17(2), 465–473.

VI Chwa, D. (2004). Sliding-mode tracking control of nonholonomic wheeled mobile robots in polar coordinates. IEEE Transactions on Control Systems Technology, 12(4), 637–644.

VII Das, T., & Kar, I. N. (2006). Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots. IEEE Transactions on Control Systems Technology, 14(3), 501–510.

VIII Ding, Y., Liu, C., Lu, S., & Zhu, Z. (2018). Hyperbolic Sliding Mode Trajectory Tracking Control of Mobile Robot. 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018).

IX Esmaeili, N., Alfi, A., & Khosravi, H. (2017). Balancing and trajectory tracking of two-wheeled mobile robot using backstepping sliding mode control: design and experiments. Journal of Intelligent & Robotic Systems, 87(3–4), 601–613.

X Fierro, R., & Lewis, F. L. (1998). Control of a nonholonomic mobile robot using neural networks. IEEE Transactions on Neural Networks, 9(4), 589–600. https://doi.org/10.1109/72.701173

XI Fukao, T., Nakagawa, H., & Adachi, N. (2000). Adaptive tracking control of a nonholonomic mobile robot. IEEE Transactions on Robotics and Automation, 16(5), 609–615.

XII Hadi, N. H. (2005). Fuzzy control of mobile robot in slippery environment. 1(2), 41–51.

XIII Hamoudi, A. K. (2016). Design and Simulation of Sliding Mode Fuzzy Controller for Nonlinear System. Journal of Engineering, 22(3), 66–76.

XIV Kanayama, Y., Kimura, Y., Miyazaki, F., & Noguchi, T. (1990). A stable tracking control method for an autonomous mobile robot. Proceedings., IEEE International Conference on Robotics and Automation, 384–389.

XV Li, Y., Wang, Z., & Zhu, L. (2010). Adaptive neural network PID sliding mode dynamic control of nonholonomic mobile robot. The 2010 IEEE International Conference on Information and Automation, 753–757.

XVI Liu, Y., Zhu, J. J., Williams II, R. L., & Wu, J. (2008). Omni-directional mobile robot controller based on trajectory linearization. Robotics and Autonomous Systems, 56(5), 461–479.

XVII Martins, F. N., Celeste, W. C., Carelli, R., Sarcinelli-Filho, M., & Bastos-Filho, T. F. (2008). An adaptive dynamic controller for autonomous mobile robot trajectory tracking. Control Engineering Practice, 16(11), 1354–1363. https://doi.org/10.1016/j.conengprac.2008.03.004

XVIII Mehrjerdi, H., & Saad, M. (2011). Chattering reduction on the dynamic tracking control of a nonholonomic mobile robot using exponential sliding mode. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 225(7), 875–886.

XIX Raja, P., & Pugazhenthi, S. (2012). Optimal path planning of mobile robots: A review. International Journal of Physical Sciences, 7(9), 1314–1320.

XX Rao, A. M., Ramji, K., Rao, B. S. K. S. S., Vasu, V., & Puneeth, C. (2017). Navigation of non-holonomic mobile robot using neuro-fuzzy logic with integrated safe boundary algorithm. International Journal of Automation and Computing, 14(3), 285–294.

XXI Samson, C., & Ait-Abderrahim, K. (1991). Feedback control of a nonholonomic wheeled cart in cartesian space. Proceedings. 1991 IEEE International Conference on Robotics and Automation, 1136–1137.

XXII Saud, L. J., & Hasan, A. F. (2018). Design of an Optimal Integral Backstepping Controller for a Quadcopter. Journal of Engineering, 24(5), 46–65.

XXIII Umar, S. N. H., Bakar, E. A., Soaid, M. S., & Samad, Z. (2014). Study on multi tasks of line following differential wheeled mobile robot for in-class project. International Journal of Modelling, Identification and Control, 21(1), 47–53.

XXIV Wu, X., Jin, P., Zou, T., Qi, Z., Xiao, H., & Lou, P. (2019). Backstepping trajectory tracking based on fuzzy sliding mode control for differential mobile robots. Journal of Intelligent & Robotic Systems, 96(1), 109–121.

XXV Xu, Y. (2008). Chattering free robust control for nonlinear systems. IEEE Transactions on Control Systems Technology, 16(6), 1352–1359.

XXVI Yang, J.-M., & Kim, J.-H. (1999). Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots. IEEE Transactions on Robotics and Automation, 15(3), 578–587.

XXVII Young, K. D., Utkin, V. I., & Ozguner, U. (1999). A control engineer’s guide to sliding mode control. IEEE Transactions on Control Systems Technology, 7(3), 328–342.

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Abstract:

This paper presents the influence of cycling impact loading and temperature on dynamic crack propagation in thin plates for two types of aluminum plates (7075, 6061) with aspect ratio (1.5,2) and plate boundary conditions (CSCS& SFSF). Using analytical solution and numerical analysis, crack lengths have (3, 5) mm and crack angle (45^o). Analytical solution using program (MATLAB-16), the purpose of analytical solution to get the mechanical and thermal stress with time at crack tip in thin aluminum plate, then calculate the dynamic crack propagation under the effect of these stresses. Numerical analysis using program (ANSYS-18 APDL) based on finite element method, the purpose of numerical analysis to obtain mechanical and thermal stress respect with time at the tip of the crack in thin aluminum plate, then calculate the dynamic crack propagation under the mechanical and thermal stresses effect. The results showed that the dynamic crack propagation increased as the crack length increased, and also found that the dynamic crack propagation decreased as the aspect ratio of the plate increased.

Keywords:

Stress,dynamic crack propagation,crack tip,analysis,plate,

Refference:

I E.E. Gdoutos, “Fracture Mechanics an Introduction”, 2005.
II James M. Gere, “Mechanics of Materials”, 2004.
III Hoai Nam Le, and Catherine Gardin, “Analytical calculation of the stress intensity factor in a surface cracked plate submitted to thermal fatigue loading”, Engineering Fracture Mechanics 77, PP.2354–2369, 2010.
IV Mahmut Uslu, Og˘uzhan Demir, and Ali O. Ayhan, “Surface Cracks in Finite Thickness Plates under Thermal and Displacement-Controlled loads – Part 1: Stress Intensity Factors”, Engineering Fracture Mechanics, Vol. 115, PP. 284–295, 2014.
V Katarina Maksimović, Dragi Stamenković, Mirko Maksimović, and Ivana Vasović, “Determination of Fracture Mechanics Parameters Structural Components with Surface Crack under Thermo mechanical Loads”,Scientific Technical Review, Vol.66, PP.27-33, No.3, 2016.
VI Shiwei Ge, Yafei Xu, Xiao Zhou, and Shangyu Peng, “Thermal Stress Analysis of a Continuous Rigid Frame Bridge”, Annals of Civil and Environmental Engineering, 2017.
VII T. K. Varadan and K. Bhaskar, “Analysis of Plates Theory and Problems”, Department of Aerospace Engineering, India Institution of Technology, Madras, India, 1999.
VIII F. Arace, “Simplified Models for the Analysis of Wave-Controlled Impacts”, 2005.
IX Loke Sworappa and R. Dharni, “Laminated Architectural Glass Subjected to Blast, Impact Loading”, 2005.
X L.S. Srinath, “Advanced Mechanics of Solid”,3rd Edition, McGraw-Hill, 2009.
XI M. Gosz, and B. Moram, “Stress Intensity Factors along Three Dimensional Elliptical Crack Fronts”, U. S. Department of Transportation, 1998.
XII L.L. Faulkner, “Practical Fracture Mechanics in Design”, Marcel Dekker, 2005.
XIII [59] M. Mir Zaei, “Fracture Mechanical Engineering”, TMU, 2000.
XIV Madenci, Erdogan, and Ibrahim Guven, “The finite element method and applications in engineering using ANSYS”. Springer, 2015.
XV Stolarski, Tadeusz, Yuji Nakasone, and Shigeka Yoshimoto, “Engineering analysis with ANSYS software”. Butterworth Heinemann, 2006.
XVI ANSYS Release 18.0 Documentation.
XVII ASM International Handbook, “Properties and Selection”, Vol.2, 1992.

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Abstract:

There has been challenging the pattern recognition that more attention needs to be paid to this area Offline Signature Verification (OSV), particularly when it is relied upon to popularize fully on the skillful frauds that are not accessible during the preparation. Its difficulties additionally incorporate little training tests and great intra-class divergence. At times the crude signature can incorporate additional pixel known as noises or may not be in the legitimate structure where preprocessing is obligatory. Insomuch as a signature is preprocessed accurately, it leads to a superior outcome for both signature matching and fraud disclosure.For example; an  appropriate estimation of gamma value improves the contrast of the signature image, on another hand, Pre-preparing likewise comprises binarization, noise elimination, so forth...The proposed method is for extraction features (such as ;Energy, Contrast, Entropy,and Correlation) from Offline Signature Verification System. In this paper, the data processing deals with twain parallel styles viz signature training and signature testing analysis. Insomuch as that the extracted features from a signature picture doesn't powerful, this will cause higher verification error rates particularly for skillful fabrications in hacking the system.The results show that’s the (UTSig) and the combination of (NISDCC, CEDAR, SigComp2012).Comparing with the other researches, the results in this Paper is the best and the system is more efficientwith (UTSig) signature which were 97%.

Keywords:

Offline Signature Verification,Insomuch,estimation of gamma value,twain parallel styles,UTSig,NISDCC,CEDAR,SigComp2012,

Refference:

I Ahmed, Z. J. (2018). Fingerprints Matching Using the Energy and Low Order Moment of Haar Wavelet Subbands. Journal of Theoretical and Applied Information Technology, 96(18), 6191–6202.

II AL-OBIADIE, S. N. M. (2016). Emotion Detection Using Facial Image Based on Geometric Attributes. University of Baghdad.

III Aldhaher, E., & George, L. (2014). Detection of Diabetic Maculopathy Using Image Analysis Techniques -Introduction and Implementation.

IV Eds, A. D. H. (2018). New Trends in Information and Communications Technology Applications (Vol. 938). https://doi.org/10.1007/978-3-030-01653-1

V Ellen, D., Day, S., & Davies, C. (2018). Scientific examination of documents: methods and techniques. CRC Press.

VI Fadhil, R., & George, L. E. (2017). Finger Vein Identification and Authentication System. LAP Lambert Academic Publishing.

VII Ferrer, M. A., Alonso, J. B., & Travieso, C. M. (2005). Offline geometric parameters for automatic signature verification using fixed-point arithmetic. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6), 993–997.

VIII George, L. E., Al-Daamy, N., Al-Daamy, S. A., & Ahmed, R. K. (2016). The using of graylevel co-occurrence matrix for features extruction of the breast cancer biopcy image (glcm). Int. J. Engg. Res. and Sci. & Tech, 5(1).

IX Gunjal, S. N., Dange, B. J., & Brahmane, A. V. (2016). Offline Signature Verification using Feature Point Extraction. International Journal of Computer Applications, 975, 8887.

X Hafemann, L. G. (2019). Learning features for Offline Handwritten Signature Verification by MANUSCRIPT-BASED THESIS PRESENTED TO ÉCOLE DE IN PARTIAL FULFILLMENT FOR THE DEGREE OF.

XI Hamza, R. M., & Al-Assadi, T. A. (2012). Genetic algorithm to find optimalGLCM features. Department of Computer Science College of Information Technology.

XII HASSAN, E. K. H., GEORGE, L. E., & MOHAMMED, F. G. (2018). Color image compression based on DCT, differential pulse coding modulation, and adaptive shift coding. Journal of Theoretical and Applied Information Technology, 96(11), 3160–3171.

XIII Inamdar, V. S., Rege, P. P., & Arya, M. S. (2010). Offline Handwritten Signature based Blind Biometric Watermarking and Authetication Technique using Biorthogonal Wavelet Transform. International Journal of Computer Applications, 11(1), 19–27. https://doi.org/10.5120/1547-1970

XIV Jabur, Z. F., & Ali, S. K. (2014). Off line Handwritten Signature Recognition based on Fusion of Global and GLCM Features Using Fuzzy Logic. JOURNAL OF THI-QAR SCIENCE, 4(3), 151–158.

XV Karouni, A., Daya, B., & Bahlak, S. (2011). Offline signature recognition using neural networks approach. Procedia Computer Science, 3, 155–161.

XVI Kaur, H., & Kaur, S. (2014). Offline Hindi Signature Recognition Using Surf Feature Extraction and Neural Networks Approach. Ijsr. Net, 3(8), 1141–1146.

XVII Mahanta, L. B., & Deka, A. (2013). A study on handwritten signature. International Journal of Computer Applications, 79(2).

XVIII Mohammed, S. N., & George, L. E. (2016). Illumination-Invariant Facial Components Extraction Using Adaptive Contrast Enhancement Methods. Current Journal of Applied Science and Technology, 1–13.

XIX Narwade, P. N., Sawant, R. R., & Bonde, S. V. (2018). Offline handwritten signature verification using cylindrical shape context. 3D Research, 9(4), 48.

XX Pirlo, G., Impedovo, D., Fairhurst, M., Pirlo, G., Impedovo, D., & Fairhurst, M. (2014). Advances in digital handwritten signature processing: a human artefact for e-society. World Scientific Publishing Co., Inc.

XXI Pratt, W. K. (1994). Digital Image Processing. In European Journal of Engineering Education (Vol. 19). https://doi.org/10.1080/03043799408928319

XXII Radhika, K. S., & Gopika, S. (2015). Online and offline signature verification: A combined approach. Procedia Computer Science, 46, 1593–1600. https://doi.org/10.1016/j.procs.2015.02.089

XXIII Rashidi, S., Fallah, A., & Towhidkhah, F. (2012). Feature extraction based DCT on dynamic signature verification. Scientia Iranica, 19(6), 1810–1819. https://doi.org/10.1016/j.scient.2012.05.007

XXIV Shakour, A. A. (2018). Biometric Authentication and Recognition System Using Hand Palm Images. Baghdad University.

XXV Sigari, M. H., Pourshahabi, M. R., & Pourreza, H. R. (2012). An ensemble classifier approach for static signature verification based on multi-resolution extracted features. International Journal of Signal Processing, Image Processing and Pattern Recognition, 5(1), 21–36.

XXVI Sindhu, B., & Jeeva, J. B. (2013). Automated Retinal Vessel Segmentation Using Morphological Operation And Threshold. International Journal of Scientific & Engineering Research, 4(5), 1614–1617. Retrieved from http://www.ijser.org

XXVII Soleimani, A., Araabi, B. N., & Fouladi, K. (2016). Deep Multitask Metric Learning for Offline Signature Verification. Pattern Recognition Letters, 80, 84–90. https://doi.org/10.1016/j.patrec.2016.05.023

XXVIII Soleimani, A., Fouladi, K., & Araabi, B. N. (2016a). Persian offline signature verification based on curvature and gradient histograms. 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), 147–152. IEEE.

XXIX Soleimani, A., Fouladi, K., & Araabi, B. N. (2016b). UTSig: A Persian offline signature dataset. IET Biometrics, 6(1), 1–8.

XXX Soleimani, A., Fouladi, K., & Araabi, B. N. (2017). UTSig: A Persian offline signature dataset. IET Biometrics, 6(1), 1–8. https://doi.org/10.1049/iet-bmt.2015.0058

XXXI Taylor, J. K., & Cihon, C. (2004). Statistical Techniques for Data Analysis. Retrieved from https://books.google.iq/books?id=yw6JwuAclCUC

XXXII Tuama, S. A., George, L. E., Okelola, M. O., Olabode, E. O., Mbah, E. N., Attah, A. J., … Sudharmaidevi, C. R. (2019). Current Research in Science and Technology Vol. 1. Current Research in Science and Technology Vol. 1, 1–17. https://doi.org/10.9734/bpi/crst/v1

XXXIII Tuama, S., & George, L. (2016). Retina Recognition Based on Texture Analysis: Building a system for individual recognition based on vasicular retina pattern.

XXXIV V.G., Y., & Patil, A. (2014). Offline and Online Signature Verification Systems: a Survey. International Journal of Research in Engineering and Technology, 3(3), 328–332.

XXXV Widiarti, A. R. (2011). Comparing Hilditch, Rosenfeld, Zhang-Suen, and Nagendraprasad-Wang-Gupta Thinning. International Journal of Computer and Information Engineering, 5(6), 563–567.

XXXVI Younesian, T., Masoudnia, S., Hosseini, R., & Araabi, B. N. (2019). Active Transfer Learning for Persian Offline Signature Verification. (February), 234–239. https://doi.org/10.1109/pria.2019.8786013

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Abstract:

Automatic License Recognition (ALPR) has been considered significant in many applications in intelligent transport and monitoring systems. As in other tasks of the computer vision, deep learning methods (DL) were implemented recently in the ALPR context, with a focus on country-specific Iraqi councils, like German or Old and Northern.  In this work, we proposed the DL-ALPR system from the beginning in the license plate detection phase of Iraqi plates according to the latest (YOLO) convolutional layers to detect single class. Utilizing a data set of Iraqi paintings collected by the researcher, and in the second stage, the detection plates are Recognition by extracting a set of license plate features using the SIFT and SURF algorithm, then using KNN to match the plates stored in the database to match them, the data is divided into two parts, part photos: 1300 pictures, And the second part, videos of the Iraqi vehicles in different environmental conditions, and the number is 35 videos. 1300 photos were divided 70% in the training phase and 30% in the testing phase and the results obtained in the testing phase were 99.2% for LP detection and 97.14% for recognition and the total accuracy of the system was 98.17%.

Keywords:

Automatic License Recognition,deep learning methods,Iraqi plates,SIFT and SURF algorithm,training phase,testing phase,

Refference:

I. A. Khazri, “Automatic License Plate Detection & Recognition using deep learning”, towards data science, 2019. web: https://towardsdatascience.com/automatic-license-plate-detection-recognition-using-deep-learning-624def07eaaf?gi=fc80f0526b7.

II. Saharkiz, “Nearest Neighbor Algorithm Implementation and Overview ” , Code project, 2009. Web: https://www.codeproject.com/Articles/32970/K-Nearest-Neighbor-Algorithm-Implementation-and-Ov.
III. D. Hoiem, Y. Chodpathumwan, Q. Dai, “Diagnosing Error in Object Detectors”, Computer Vision – ECCV, Springer Berlin Heidelberg, Berlin, Heidelberg, Vol. 1, No. 1, Pp. 340-353, 2012.
IV. H. Bay, T. Tuytelaars, L. Van Gool, “SURF: Speeded Up Robust Features”, Computer Vision – ECCV, Springer Berlin Heidelberg, Berlin, Heidelberg, Vol. 5, No. 1, Pp. 404-417, 2006.
V. H. A.-H. Kahdum, “Leukocytes Image Segmentation and Classification Based on Geometrical Features and Naïve Bayes Classifier,” Master Degree, College of Science, University of Baghdad, Baghdad – Iraq, 2019.
VI. Kusumadewi, C.A. Sari, E.H. Rachmawanto, “License Number Plate Recognition using Template Matching and Bounding Box Method”, Journal of Physics: Conference Series, IOP Publishing, Vol. 1, No. 1, Pp. 012067,2019.
VII. J. Kim, “Automatic Vehicle License Plate Extraction Using Region-Based Convolutional Neural Networks and Morphological Operations”, Symmetry, Vol. 11, No. 7, Pp. 882,2019.

VIII. J. Redmon, A. Farhadi, “YOLO9000: better, faster, stronger”, Proceedings of the IEEE conference on computer vision and pattern recognition, Vol. 1, No. 1, Pp. 7263-7271, 2017.

IX. J.T. Pedersen, “Study group SURF: Feature detection & description”, Department of Computer Science, Aarhus University, Pp. 1-12,2011

X. Mathew, Sheena S, “A Comparison of Sift And Surf Algorithm For The Recognition of An Efficient Iris Biometric System”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, No. 1, Pp. 37–42, 2016.

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Abstract:

Intelligent access control is one of the challenging tasksin the human identification, image analysis, and diagnoses disease and computer vision. The focus towards the intelligent access control has been increased in the last years due to its various, applications in different   domains. For this reason, it was used intelligent access control to facilitate the task of identifying the human.The objective of this paper is to analyse and evaluate the seven techniques for the intelligent access control and advantage and disadvantage of each type. In addition, represents biometrics characteristics in general. The Biometric feature is used to determine human identity including the brain signals. Through this study, brain signals are the best among the techniques. In this study, we first presented a survey of the Computational intelligence techniques in biometrics. All previous studies used brain EEG signals. Where different algorithms were used to extract, the features. These feature applied for human identification. The Accuracy achieved was up to 97% according to the studies found in this research

Keywords:

Computational intelligence,human identification,Biometrics,Finger print,EEG signals,

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